Many fraud detection systems authenticate a transaction based on a risk score. These systems, typically, comprise a risk engine that generates, as the risk score, a numerical value from an evaluation of current factors in connection with the transaction. For example, the factors may relate to the time of the transaction, location from which the transaction emanated, etc. If the risk engine determines that these current factors deviate from factors relating to previous transactions then a high risk score may be generated indicative of a high risk in connection with the transaction. Conversely, if the risk engine determines that these current factors are consistent with factors relating to previous transactions then a low risk score may be generated indicative of a low risk in connection with the transaction.
Additionally, in order to handle legitimate behavioural changes, the above type of fraud detection systems constantly learns new behavioural patterns and forgets older ones. As will be appreciated, it is desirable in general to learn these changes in behavioural patterns fast such that the system does not issue alerts in connection with authentic transactions. However, there exists a problem with this approach in that the learning rate for learning new behavioural patterns is similar to the forgetting rate (i.e., the faster you learn, the faster you forget). Unfortunately, this leads to many false alerts being issued in connection with authentic transactions.
A need, therefore, exists for further improved techniques for use in authenticating transactions.